1. Technical Field
This invention relates in general to communications and, more particularly, to a method and apparatus for optimizing RSSE calculations.
2. Description of the Related Art
Compared to binary modulation commonly used in 2G standards, advanced wireless communication systems may use high-level modulation schemes. From a system point of view, this results in spectral efficiency at the expense of an increase of Inter-Symbol Interference (ISI).
In order to mitigate ISI, efficient equalizers need to be implemented on the receiver side. The optimal solution to this problem is known as Maximum Likelihood Sequence Estimation (MLSE). The goal of this method is to subtract every possible sequence of L constellation points weighted by the channel estimation factors from the received symbol vector in order to determine the most probable symbols. L corresponds to the channel estimation length.
The concept behind MLSE is to choose the minimum error square between the received symbol yk and every possible linear combination of the last L symbols âk-i weighted by the channel response samples hi, all values being complex. In a mathematical form, the approximated symbol frame of length Lframe is given by:
            a      _        ^    =      arg    ⁢          {                        min                                    a              _                        ^                          ⁢                              ∑                          k              =              0                                                      L                frame                            -              1                                ⁢                                          ⁢                      (                                                                                                y                    k                                    -                                                            ∑                                              i                        =                        0                                                                    L                        -                        1                                                              ⁢                                                                                  ⁢                                                                  h                        i                                            ⁢                                                                                                    a                            ^                                                                                k                            -                            i                                                                          ⁡                                                  (                          s                          )                                                                                                                                                2                        )                              }      
This inner term of the equation, the branch metric, can be rewritten as:
                        y        k            -                        ∑                      i            =            1                                L            -            1                          ⁢                                  ⁢                              h            i                    ⁢                                                    a                ^                                            k                -                i                                      ⁡                          (              s              )                                          -                        h          0                ⁢                                            a              ^                        k                    ⁡                      (            s            )                                    2
The branch metric (bm) is composed by three terms. The first one is the soft value of the received symbol, the second one is the estimated contribution of the ISI coming from the L-1 preceding symbols and the third one is the supposed received symbol multiplied by a channel coefficient.
The âk-i(s) for i=1, . . . ,L−1 fully determine a state of the MLSE in a trellis representation. Thus, the number of possible combinations or states, taking into account that âk-i(s) belongs to a set of M symbols and that K=L−1 with the channel response being of length L, is equal to MK. Furthermore, M transitions output each state. This makes MLSE too complicated for implementation in terminals receivers.
A more adapted method, the Reduced State Sequence Equalizer (RSSE) is derived from MLSE. The simplification lies in the reduction of the number of states. Like for the MLSE, the RSSE method consists in minimizing a sum of square values (branch metrics) among all the symbols of the frame under consideration. Therefore, a trellis structure is well adapted to carry out this task. Compared to convolutional decoding, there are, for the general case, no redundancy properties among the branch metrics.
For each symbol received, i.e. on trellis transition, the following units are executed: (1) Branch Metric Computation (BMC) Unit: Compute individually one branch metric per transition, (2) Add Compare and Select (ACS) Unit. With more than two transitions per state and (3) Post-Processing. Current implementations do not use generic structure to perform the BMC and ACS functions. Each branch metric is computed, added to the previous state value and the minimum path is selected for all next states.
While RSSE requires only a fraction of calculations required by MLSE, it still is computation intensive, using significant processing resources. Therefore, a need has arisen for a method and apparatus for performing RSSE computations efficiently.